In this study a Q-sort was used to measure differences in academic and public librarian attitudes toward computers, desktop publishing systems and expert systems. Fifty-two academic and sixty-two public librarians responded. The responses were sorted into groups based on the type of library and whether or not the respondent followed the stated Q-sort distribution.

<p>Many artificial intelligence applications suffer severely from a bottleneck in acquiring domain information necessary to go beyond toy hand-built demonstrations to realistic applications. This project examines one approach to reducing that bottleneck by using automated and semi-automated techniques to analyze published domain-relevant material. A taxonomy of terms related to computers with an emphasis on microprocessors is developed and presented. The methods used are experimental and not yet fully validated, but are potentially of great use for extracting useful domain information from published material. Preliminary validation by comparison with a published taxonomy shows that these methods have produced a taxonomy which is better suited for the immediate use of this taxonomy.</p> / Master of Science

A project report submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. / This dissertation has as it's objective to develop an expert system for the purpose of investigating the suitability of using expert systems in developing areas. It is the first time to our knowledge that an expert system has been tested for usage in such a context. The housing and more specifically roofing domain is investigated for an application topic resulting in "the selection of suitable roofing types" being chosen. Potential end-users are identified, an appropriate context of such a system is outlined and an expert system tool is selected, The knowledge engineering and building of the knowledge base are described. Feedback from the parties involved in testing and validating BUILDROOF is documented. In addition the suitability of using expert systems in the developing areas context in which BUILDROOF was developed, is discussed. Finally, recommendations regarding potentially suitable areas of application for expert systems in developing areas are outlined. / Andrew Chakane 2019

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<p>A runway incursion occurs when an aircraft, ground vehicle, or a pedestrian is present on a runway when they were not supposed to be there. Runway incursions are a decades-old and continuing problem. The runway incursion between two Boeing 747s at Tenerife airport in 1977 is still the worst accident in aviation history. Despite the aviation community’s efforts to mitigate runway incursions, the number of incursions has not decreased. Though most of the runway incursions that occur today are near-misses or incidents, and do not result in injuries or aircraft damage, we cannot count on fortune to prevent another deadly accident.</p><p>While the COVID-19 crisis has slowed air traffic, the industry is optimistic about recovery and return to the growth in air traffic we have seen over the past decade. With this growth comes the potential for more runway incursions. Therefore, we must develop better ways of preventing incursions. Runway incursion incidents are one way to learn more about how we can prevent similar incidents in the future and reduce the probability of serious accidents. Unfortunately, most incident reports lack detailed information on the causes of runway incursions. In the United States, trained investigators at the National Transportation Safety Board investigate aviation accidents, but not most incidents, including incursions. Air traffic controllers on duty at the time of incursion report the incident to the FAA. While most controller reports explain what happened, they often do not explain why the incident happened. We need deeper insight into why incidents occur so that we can develop more effective measures to reduce incursions.</p><p>After controllers submit their incident reports, reviewers at the FAA go through the controller-generated reports and determine the need for further investigation. They may contact the controllers for more information or talk to the pilots involved. This research considers one aspect of the reporting process — the reporting form. The research hypothesis is that an alternative reporting form that asks detailed questions and guides the controller to look deeper into an incident can provide more details on human error and causes of these errors than the current form, which does not necessarily prompt controllers to gather all the details of the incident.</p><p>The design of the alternative reporting form is based on the theoretical framework of expert systems. Expert systems, which provide tailored questions and guidance to medical doctors and others, have proven useful in other fields. The resulting alternative tool aims to guide controllers into answering three major questions: what happened (which aircraft were where, and when), how it happened (e.g., controller gave the wrong instruction), and why it happened (e.g., controller was fatigued).</p><p>To investigate how controllers interact with different reporting formats and what helps them or detracts them from creating useful reports, the research experiment involved controllers reporting two hypothetical runway incursions either using the alternative reporting tool or an online survey based on the current FAA form. The experiment used surveys, think-aloud protocols, observations, and interviews to collect data on what controllers included in their reports and how controllers generated these reports. The findings helped compare the type of information we get from the two reporting formats, and how the reporting formats affected the quality of the incident reports.</p><p>Overall, the alternative tool-generated reports provided more information than the online survey based on the current FAA form. Each controller who participated in the experiment approached preparing an incident report differently and different factors motivated them to specify details of the incident. While the format of the alternative reporting form helped one controller talk to the pilot and learn more about why the pilot made an error, the format did not have the same impact on another controller.</p><p>This research identifies ways of helping controllers prepare more useful reports. This research may help the FAA improve data collection. More useful reports in the future can help the aviation community identify the cause of human errors leading to incursions, and develop more effective mitigation strategies, ultimately saving lives.</p><p><br></p>

This research studies the process of acquiring knowledge from experts; that is, studies knowledge-acquisition methods to acquire expert knowledge. Forty subjects used a machine-aided knowledge-acquisition tool to model a word processing task. By using the tool, the subjects developed models that were on average 72.8% accurate with a baseline model of the task and 88.5 % consistent among themselves. This research makes four contributions: 1) a complete review of thirty-one knowledge-acquisition methods from manual to machine learning, 2) an evaluation methodology and metrics to evaluate knowledge-acquisition methods, 3) an evaluation of an automated knowledge-acquisition tool called Cognitive Analysis Tool (CAT) developed for this research, and 4) suggested improvements to the current version of the tool. This research describes, develops a taxonomy of, and evaluates thirty-one knowledge-acquisition methods to determine which method matches a defined set of criteria A method is chosen, extended, and automated in the form of a machine-aided knowledge-acquisition tool. The method is chosen based on five criteria including a connection between the chosen, method and the information processing model of problem solving as defined by Newell and Simon (1972). This research evaluates the performance of the tool in terms of the accuracy and consistency of the knowledge bases generated by using the tool. A baseline is derived from this study to which other knowledge-acquisition tools' performance can be compared. The evaluation methodology and metrics developed in this research can be used to evaluate other knowledge-acquisition tools. From this research, four groups of changes to the automated knowledge-acquisition tool are suggested to improve the usability and performance of the tool. The changes are suggested for the user interface and the modes of operation of the tool. / Master of Science

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